Academic Journal of Computing & Information Science, 2021, 4(5); doi: 10.25236/AJCIS.2021.040512.
Yan Fan1, Yilan Wu2,3
1Institute for Advanced Studies in Humanities and Social Science, Chongqing University, Chongqing, China
2Criminal Law School, East China University of Political Science and Law, Shanghai, China
3Police Department, Anhui Vocational College of Police Officers, Hefei, China
According to the rapid development of the global economy and the continuous improvement of people's living standards, more and more people have a car driving license. We must pay attention to standardize the driving behavior of drivers, which is important to ensure personal safety. The survey also found that more than half of traffic accidents are related to dangerous driving behavior. However, in judicial judgment, there are many problems in judicial application which can not be solved because of the abstract provisions of laws in various countries. If the method of big data can be used as a reference for determining risky driving behaviors of drivers, it can provide evidence for identification, improve the accuracy of judicial judgment, and timely avoid some accidents. This paper aims to explore what application technical support is needed by judicial big data in determining risky driving behaviors of drivers.
judicial big data, risky driving, application technology requirements
Yan Fan, Yilan Wu. Technical Demand Analysis of Judicial Big Data Application under Risky Driving Behavior of Drivers. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 5: 85-90. https://doi.org/10.25236/AJCIS.2021.040512.
[1] Jiayun, W. (2020). Judicial Big Data and the Realization of Judicial Justice. Journal of Jishou University (Social Sciences Edition), 41(2), 137.
[2] Devins, C., Felin, T., Kauffman, S., & Koppl, R. (2017). The law and big data. Cornell JL & Public Policy, 27, 357.
[3] Crawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. BCL Rev., 55, 93.
[4] Lyon, D. (2014). Surveillance, Snowden, and big data: Capacities, consequences, critique. Big data & society, 1(2), 2053951714541861.
[5] Medvedeva, M., Vols, M., & Wieling, M. (2018). Judicial decisions of the European Court of Human Rights: Looking into the crystal ball. In Proceedings of the Conference on Empirical Legal Studies.
[6] Botsman, R. (2017). Big data meets Big Brother as China moves to rate its citizens. Wired UK, 21, 1-11.
[7] Oviedo-Trespalacios, O., Nandavar, S., Watson, B., Lewis, I., Haque, S., White, K., ... & O'Donnell, K. (2019, September). Benchmarking distracted driving against other key risky driving behaviours. In 2019 Australasian Road Safety Conference.
[8] Zimbardo, P. G., Keough, K. A., & Boyd, J. N. (1997). Present time perspective as a predictor of risky driving. Personality and individual differences, 23(6), 1007-1023.
[9] Schwebel, D. C., Ball, K. K., Severson, J., Barton, B. K., Rizzo, M., & Viamonte, S. M. (2007). Individual difference factors in risky driving among older adults. Journal of safety research, 38(5), 501-509.
[10] Lamba, H. S., & Dubey, S. K. (2015, September). Analysis of requirements for big data adoption to maximize IT business value. In 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions) (pp. 1-6). IEEE.
[11] Burmester, G., Ma, H., Steinmetz, D., & Hartmannn, S. (2018). Big data and data analytics in aviation. In Advances in Aeronautical Informatics (pp. 55-65). Springer, Cham.
[12] Liu, Y. (2018, January). Big data technology and its analysis of application in urban intelligent transportation system. In 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) (pp. 17-19). IEEE.
[13] Hutchins, M. G., McGrane, S. J., Miller, J. D., Hagen‐Zanker, A., Kjeldsen, T. R., Dadson, S. J., & Rowland, C. S. (2017). Integrated modeling in urban hydrology: reviewing the role of monitoring technology in overcoming the issue of ‘big data’requirements. Wiley Interdisciplinary Reviews: Water, 4(1), e1177.
[14] Kohlwey, E., Sussman, A., Trost, J., & Maurer, A. (2011, July). Leveraging the cloud for big data biometrics: Meeting the performance requirements of the next generation biometric systems. In 2011 IEEE World Congress on Services (pp. 597-601). IEEE.
[15] Boehm, B. (2000). Requirements that handle IKIWISI, COTS, and rapid change. Computer, 33(7), 99-102.
[16] Cui, B., Mei, H., & Ooi, B. C. (2014). Big data: the driver for innovation in databases. National Science Review, 1(1), 27-30.
[17] Terzi, R., Sagiroglu, S., & Demirezen, M. U. (2018). Big data perspective for driver/driving behavior. IEEE Intelligent Transportation Systems Magazine, 12(2), 20-35.